Atlas

In its present (and non definitve) form, the F-TRACT atlas is provided as csv files that can be read in any table editor. In addtion, we provide a Matlab routine allowing to read the features of the atlas as Matlab variables. The atlas is provided for free use for research use only, with limited accuracy, which hopefully will improve with the different releases. Please cite David et al. (2013) Probabilistic functional tractography of the human cortex, NeuroImage, and Trebaul et al. (submitted) Probabilistic functional tractography of the human cortex revisited, when using the F-TRACT atlas.

F-TRACT atlas releases

June 2017

f-tract_v1706_marsatlas: Connectivity probablility and peak latency estimated from CCEP data recorded in 174 patients, only in the MarsAtlas parcellation scheme.

August 2017

f-tract_v1708_marsatlas: Connectivity probability with associated p-values as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 174 patients, in the MarsAtlas parcellation scheme. The CCEP features are: peak and onset latency (LatStart), amplitude, integral, duration and the velocity estimated from the onset latency and the Euclidean distance between the parcels.

Probability and latency maps: Images representing the connectivity probablility and peak latency for all the regions in the MarsAtlas parcellation.

December 2017

f-tract_v1712_marsatlas: Connectivity probability with associated p-values as well as features describing fibers biophysical properties, estimated from CCEP data recorded in 213 patients, in the MarsAtlas, Brodmann, AAL and MaxProbMap parcellation schemes. The CCEP features are: peak and onset latency (LatStart), amplitude, integral, duration and the velocity estimated from the onset latency and the fibers distance between the parcels.

Features maps: Images representing the connectivity probablility and response features for all the regions in the MarsAtlas parcellation.

How to use this atlas

Load the matrices

Download the .csv files : one corresponds to the lateralised and the other to the symmetrical atlas.

Use read_csv_atlas with the path to the csv file you want to read, and specify if it corresponds to the symmetrical (merged = true) or lateralised (merged = false) matrices.

In the first release, you have access to the latency of the first response peak, the connection probability and the corresponding pvalue matrices as well as the list of the parcels of the atlas used. In the second release, you also have the CCEP features: the amplitude, the duration, the integral, the onset latency and the velocity. The plot_matrix function allows you to plot the matrix in the same way as in Trebaul et al. (submitted).

Visualize the maps with BrainVISA

Download the maps for the a chosen parcellation. You will have the meshes, for each hemisphere of the inflated and non-inflated brain, and the textures corresponding to the stimulation of each parcel.Textures filename type : 'L_ACC_left' : 'L_ACC' corresponds to the stimulated parcel; here the left anterior cingulate cortex, and 'left' accounts for the visualization of the recording hemisphere; here you will see the parcels of the left one.

Open BrainVISA.

Go to Viewers > Anatomist Show Mesh and run the mesh of the hemisphere you want to look at. Here, to visualize L_ACC_left.gii, we choose inflated_left_white_ftract.gii. The Anatomist window will open up.

Click on File > Open and choose the texture of the stimulated parcel you want. We choose L_ACC_left.gii.

Select both the mesh and the texture and click on Fusion.

Open the textured surface by moving it to '3D' and explore the map. For the first release, the first image represents the latency and the second one the probability. For the second release, the features appear in the following order: 1) amplitude, 2) duration, 3) integral, 4) onset latency, 5) peak latency, 6) probability, 7) p-value, 8) velocity.

You can adjust the colorbar with a right click on the texture > Color > Pallet. Choose a fusion pallet in order not to display the parcels for which we haven't got enough data to represent it.